Ghat: An R package for identifying adaptive polygenic traits

AbstractIdentifying selection on polygenic complex traits in crops and livestock is key to understanding evolution and helps prioritize important characteristics for breeding. However, the QTL that contribute to polygenic trait variation exhibit small or infinitesimal effects. This hinders the ability to detect QTL controlling polygenic traits because enormously high statistical power is needed for their detection. Recently, we circumvented this challenge by introducing a method to identify selection on complex traits by evaluating the relationship between genome-wide changes in allele frequency and estimates of effect-size. The method involves calculating a composite-statistic across all markers that captures this relationship, followed by implementing a linkage disequilibrium-aware permutation test to evaluate if the observed pattern differs from that expected due to drift during evolution and population stratification. In this manuscript, we describe “Ghat”, an R package developed to implement the method to test for selection on polygenic traits. We demonstrate the package by applying it to test for polygenic selection on 15 published European winter wheat traits including yield, biomass, quality, morphological characteristics, and disease resistance traits. The results highlight the power of Ghat to identify selection on complex traits. The Ghat package is accessible on CRAN, The Comprehensive R Archival Network, and on GitHub.

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A powerful and efficient two-stage method for detecting gene-to-gene interactions in GWAS

Biostatistics ◽

10.1093/biostatistics/kxw060 ◽

2017 ◽

Vol 18 (3) ◽

pp. 477-494 ◽

Cited By ~ 5

Author(s):

Jakub Pecanka ◽

Marianne A. Jonker ◽

Zoltan Bochdanovits ◽

Aad W. Van Der Vaart ◽

Keyword(s):

Complex Traits ◽

Multiple Testing ◽

Statistical Power ◽

Genome Wide Association Study ◽

Score Test ◽

Interaction Model ◽

Type I ◽

Two Stage ◽

Genome Wide ◽

Strong Control

Summary For over a decade functional gene-to-gene interaction (epistasis) has been suspected to be a determinant in the “missing heritability” of complex traits. However, searching for epistasis on the genome-wide scale has been challenging due to the prohibitively large number of tests which result in a serious loss of statistical power as well as computational challenges. In this article, we propose a two-stage method applicable to existing case-control data sets, which aims to lessen both of these problems by pre-assessing whether a candidate pair of genetic loci is involved in epistasis before it is actually tested for interaction with respect to a complex phenotype. The pre-assessment is based on a two-locus genotype independence test performed in the sample of cases. Only the pairs of loci that exhibit non-equilibrium frequencies are analyzed via a logistic regression score test, thereby reducing the multiple testing burden. Since only the computationally simple independence tests are performed for all pairs of loci while the more demanding score tests are restricted to the most promising pairs, genome-wide association study (GWAS) for epistasis becomes feasible. By design our method provides strong control of the type I error. Its favourable power properties especially under the practically relevant misspecification of the interaction model are illustrated. Ready-to-use software is available. Using the method we analyzed Parkinson’s disease in four cohorts and identified possible interactions within several SNP pairs in multiple cohorts.

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Protestant Ethic and Performance: A Re-Examination

Psychological Reports ◽

10.2466/pr0.1981.48.1.335 ◽

1981 ◽

Vol 48 (1) ◽

pp. 335-338 ◽

Cited By ~ 16

Author(s):

Daniel C. Ganster

Keyword(s):

Statistical Power ◽

Work Performance ◽

Protestant Ethic ◽

Sorting Task ◽

And Performance ◽

And Behavior ◽

Attitudes And Behavior ◽

The Relationship ◽

Ethic Scale ◽

High Statistical Power

An experiment was conducted to examine the relationship between endorsement of the Protestant Ethic and work performance and satisfaction. Employing an electronic sorting task with 95 subjects, the study did not confirm earlier findings of Merrens and Garrett (1975), despite high statistical power. Results question the interpretation of the Protestant Ethic scale as an indicant of work attitudes and behavior.

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Across-cohort QC analyses of genome-wide association study summary statistics from complex traits

10.1101/033787 ◽

2015 ◽

Author(s):

Guo-Bo Chen ◽

Sang Hong Lee ◽

Matthew R Robinson ◽

Maciej Trzaskowski ◽

Zhi-Xiang Zhu ◽

...

Keyword(s):

Complex Traits ◽

Statistical Power ◽

Association Studies ◽

False Negative ◽

Genome Wide Association ◽

Effect Sizes ◽

Genome Wide Association Studies ◽

Summary Statistics ◽

Unknown Sample ◽

Genome Wide

Genome-wide association studies (GWASs) have been successful in discovering replicable SNP-trait associations for many quantitative traits and common diseases in humans. Typically the effect sizes of SNP alleles are very small and this has led to large genome-wide association meta-analyses (GWAMA) to maximize statistical power. A trend towards ever-larger GWAMA is likely to continue, yet dealing with summary statistics from hundreds of cohorts increases logistical and quality control problems, including unknown sample overlap, and these can lead to both false positive and false negative findings. In this study we propose a new set of metrics and visualization tools for GWAMA, using summary statistics from cohort-level GWASs. We proposed a pair of methods in examining the concordance between demographic information and summary statistics. In method I, we use the population genetics Fststatistic to verify the genetic origin of each cohort and their geographic location, and demonstrate using GWAMA data from the GIANT Consortium that geographic locations of cohorts can be recovered and outlier cohorts can be detected. In method II, we conduct principal component analysis based on reported allele frequencies, and is able to recover the ancestral information for each cohort. In addition, we propose a new statistic that uses the reported allelic effect sizes and their standard errors to identify significant sample overlap or heterogeneity between pairs of cohorts. Finally, to quantify unknown sample overlap across all pairs of cohorts we propose a method that uses randomly generated genetic predictors that does not require the sharing of individual-level genotype data and does not breach individual privacy.

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On prs for complex polygenic trait prediction

10.1101/447797 ◽

2018 ◽

Cited By ~ 2

Author(s):

Bingxin Zhao ◽

Fei Zou

Keyword(s):

Complex Traits ◽

Prediction Accuracy ◽

Association Studies ◽

Prediction Method ◽

Causal Snps ◽

Polygenic Risk Score ◽

Genome Wide Association Studies ◽

Genome Wide ◽

Polygenic Traits ◽

Low Performance

Polygenic risk score (PRS) is the state-of-art prediction method for complex traits using summary level data from discovery genome-wide association studies (GWAS). The PRS, as its name suggests, is designed for polygenic traits by aggregating small genetic effects from a large number of causal SNPs and thus is viewed as a powerful method for predicting complex polygenic traits by the genetics community. However, one concern is that the prediction accuracy of PRS in practice remains low with little clinical utility, even for highly heritable traits. Another practical concern is whether genome-wide SNPs should be used in constructing PRS or not. To address the two concerns, we investigate PRS both empirically and theoretically. We show how the performance of PRS is influenced by the triplet (n, p, m), where n, p, m are the sample size, the number of SNPs studied, and the number of true causal SNPs, respectively. For a given heritability, we find that i) when PRS is constructed with all p SNPs (referred as GWAS-PRS), its prediction accuracy is controlled by the p/n ratio; while ii) when PRS is built with a set of top-ranked SNPs that pass a pre-specified threshold (referred as threshold-PRS), its accuracy varies depending on how sparse the true genetic signals are. Only when m is magnitude smaller than n, or genetic signals are sparse, can threshold-PRS perform well and outperform GWAS-PRS. Our results demystify the low performance of PRS in predicting highly polygenic traits, which will greatly increase researchers’ aware-ness of the power and limitations of PRS, and clear up some confusion on the clinical application of PRS.

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An atlas of genetic associations in UK Biobank

10.1101/176834 ◽

2017 ◽

Cited By ~ 18

Author(s):

Oriol Canela-Xandri ◽

Konrad Rawlik ◽

Albert Tenesa

Keyword(s):

Complex Traits ◽

Statistical Power ◽

Association Studies ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Uk Biobank ◽

Genetic Associations ◽

Genome Wide ◽

Related Individuals ◽

Sufficient Statistical Power

ABSTRACTGenome-wide association studies have revealed many loci contributing to the variation of complex traits, yet the majority of loci that contribute to the heritability of complex traits remain elusive. Large study populations with sufficient statistical power are required to detect the small effect sizes of the yet unidentified genetic variants. However, the analysis of huge cohorts, like UK Biobank, is complicated by incidental structure present when collecting such large cohorts. For instance, UK Biobank comprises 107,162 third degree or closer related participants. Traditionally, GWAS have removed related individuals because they comprised an insignificant proportion of the overall sample size, however, removing related individuals in UK Biobank would entail a substantial loss of power. Furthermore, modelling such structure using linear mixed models is computationally expensive, which requires a computational infrastructure that may not be accessible to all researchers. Here we present an atlas of genetic associations for 118 non-binary and 599 binary traits of 408,455 related and unrelated UK Biobank participants of White-British descent. Results are compiled in a publicly accessible database that allows querying genome-wide association summary results for 623,944 genotyped and HapMap2 imputed SNPs, as well downloading whole GWAS summary statistics for over 30 million imputed SNPs from the Haplotype Reference Consortium panel. Our atlas of associations (GeneATLAS,http://geneatlas.roslin.ed.ac.uk) will help researchers to query UK Biobank results in an easy way without the need to incur in high computational costs.

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bWGR: Bayesian whole-genome regression

Bioinformatics ◽

10.1093/bioinformatics/btz794 ◽

2019 ◽

Cited By ~ 1

Author(s):

Alencar Xavier ◽

William M Muir ◽

Katy M Rainey

Keyword(s):

Bayesian Methods ◽

Expectation Maximization ◽

Complex Traits ◽

Hierarchical Models ◽

R Package ◽

Supplementary Information ◽

Whole Genome ◽

Regression Methods ◽

Genome Wide ◽

User Friendly

AbstractMotivationWhole-genome regressions methods represent a key framework for genome-wide prediction, cross-validation studies and association analysis. The bWGR offers a compendium of Bayesian methods with various priors available, allowing users to predict complex traits with different genetic architectures.ResultsHere we introduce bWGR, an R package that enables users to efficient fit and cross-validate Bayesian and likelihood whole-genome regression methods. It implements a series of methods referred to as the Bayesian alphabet under the traditional Gibbs sampling and optimized expectation-maximization. The package also enables fitting efficient multivariate models and complex hierarchical models. The package is user-friendly and computational efficient.Availability and implementationbWGR is an R package available in the CRAN repository. It can be installed in R by typing: install.packages(‘bWGR’).Supplementary informationSupplementary data are available at Bioinformatics online.

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Neighbor GWAS: incorporating neighbor genotypic identity into genome-wide association studies of field herbivory

Heredity ◽

10.1038/s41437-020-00401-w ◽

2021 ◽

Author(s):

Yasuhiro Sato ◽

Eiji Yamamoto ◽

Kentaro K. Shimizu ◽

Atsushi J. Nagano

Keyword(s):

Complex Traits ◽

Association Studies ◽

Field Studies ◽

R Package ◽

Genome Wide Association ◽

Effective Range ◽

Individual Plant ◽

Genome Wide Association Studies ◽

Genome Wide ◽

Neighbor Effects

AbstractAn increasing number of field studies have shown that the phenotype of an individual plant depends not only on its genotype but also on those of neighboring plants; however, this fact is not taken into consideration in genome-wide association studies (GWAS). Based on the Ising model of ferromagnetism, we incorporated neighbor genotypic identity into a regression model, named “Neighbor GWAS”. Our simulations showed that the effective range of neighbor effects could be estimated using an observed phenotype when the proportion of phenotypic variation explained (PVE) by neighbor effects peaked. The spatial scale of the first nearest neighbors gave the maximum power to detect the causal variants responsible for neighbor effects, unless their effective range was too broad. However, if the effective range of the neighbor effects was broad and minor allele frequencies were low, there was collinearity between the self and neighbor effects. To suppress the false positive detection of neighbor effects, the fixed effect and variance components involved in the neighbor effects should be tested in comparison with a standard GWAS model. We applied neighbor GWAS to field herbivory data from 199 accessions of Arabidopsis thaliana and found that neighbor effects explained 8% more of the PVE of the observed damage than standard GWAS. The neighbor GWAS method provides a novel tool that could facilitate the analysis of complex traits in spatially structured environments and is available as an R package at CRAN (https://cran.rproject.org/package=rNeighborGWAS).

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Widespread allelic heterogeneity in complex traits

10.1101/076984 ◽

2016 ◽

Cited By ~ 3

Author(s):

Farhad Hormozdiari ◽

Anthony Zhu ◽

Gleb Kichaev ◽

Ayellet V. Segrè ◽

Chelsea J.-T. Ju ◽

...

Keyword(s):

Complex Traits ◽

Statistical Power ◽

Association Studies ◽

Density Lipoprotein ◽

Computational Method ◽

Allelic Heterogeneity ◽

Genome Wide Association Studies ◽

New Methods ◽

Genome Wide ◽

Causal Variants

AbstractRecent successes in genome-wide association studies (GWASs) make it possible to address important questions about the genetic architecture of complex traits, such as allele frequency and effect size. One lesser-known aspect of complex traits is the extent of allelic heterogeneity (AH) arising from multiple causal variants at a locus. We developed a computational method to infer the probability of AH and applied it to three GWAS and four expression quantitative trait loci (eQTL) datasets. We identified a total of 4152 loci with strong evidence of AH. The proportion of all loci with identified AH is 4-23% in eQTLs, 35% in GWAS of High-Density Lipoprotein (HDL), and 23% in schizophrenia. For eQTLs, we observed a strong correlation between sample size and the proportion of loci with AH (R2=0.85, P = 2.2e-16), indicating that statistical power prevents identification of AH in other loci. Understanding the extent of AH may guide the development of new methods for fine mapping and association mapping of complex traits.

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Genome-wide analyses in 1,987,836 participants identify 39 genetic loci associated with sleep apnoea

10.1101/2020.09.29.20199893 ◽

2020 ◽

Author(s):

Adrian I Campos ◽

Nathan Ingold ◽

Yunru Huang ◽

Pik Fang Kho ◽

Xikun Han ◽

...

Keyword(s):

Complex Traits ◽

Statistical Power ◽

Genome Wide Association Study ◽

Meta Analysis ◽

Genetic Correlations ◽

Sleep Apnoea ◽

Risk Scores ◽

Genetic Associations ◽

Genome Wide ◽

A Genome

Rationale: Sleep apnoea is a complex disorder characterised by periods of halted breathing during sleep. Despite its association with serious health conditions such as cardiovascular disease, the aetiology of sleep apnoea remains understudied, and previous genetic studies have failed to identify replicable genetic risk factors. Objective: To advance our understanding of factors that increase susceptibility to sleep apnoea by identifying novel genetic associations. Methods: We conducted a genome-wide association study (GWAS) meta-analysis of sleep apnoea across five cohorts, and a previously published GWAS of apnoea-hypopnea index (N Total =510,484). Further, we used multi-trait analysis of GWAS (MTAG) to boost statistical power, leveraging the high genetic correlations between apnoea, snoring and body mass index. Replication was performed in an independent sample from 23andMe, Inc (N Total =1,477,352; N cases =175,522). Results: Our results revealed 39 independent genomic loci robustly associated with sleep apnoea risk, and significant genetic correlations with multisite chronic pain, sleep disorders, diabetes, high blood pressure, osteoarthritis, asthma and BMI-related traits. We also derived polygenic risk scores for sleep apnoea in a leave-one-out independent cohort and predicted probable sleep apnoea in participants (OR=1.15 to 1.22; variance explained = 0.4 to 0.9%). Conclusions: We report novel genetic markers robustly associated with sleep apnoea risk and substantial molecular overlap with other complex traits, thus advancing our understanding of the underlying biological mechanisms of susceptibility to sleep apnoea.

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Neighbor GWAS: incorporating neighbor genotypic identity into genome-wide association studies of field herbivory

10.1101/845735 ◽

2019 ◽

Cited By ~ 1

Author(s):

Yasuhiro Sato ◽

Eiji Yamamoto ◽

Kentaro K. Shimizu ◽

Atsushi J. Nagano

Keyword(s):

Complex Traits ◽

Association Studies ◽

Field Studies ◽

R Package ◽

Genome Wide Association ◽

Effective Range ◽

Individual Plant ◽

Genome Wide Association Studies ◽

Genome Wide ◽

Neighbor Effects

ABSTRACTAn increasing number of field studies have shown that the phenotype of an individual plant depends not only on its genotype but also on those of neighboring plants; however, this fact is not taken into consideration in genome-wide association studies (GWAS). Based on the Ising model of ferromagnetism, we incorporated neighbor genotypic identity into a regression model, named “Neighbor GWAS”. Our simulations showed that the effective range of neighbor effects could be estimated using an observed phenotype from when the proportion of phenotypic variation explained (PVE) by neighbor effects peaked. The spatial scale of the first nearest neighbors gave the maximum power to detect the causal variants responsible for neighbor effects, unless their effective range was too broad. However, if the effective range of the neighbor effects was broad and minor allele frequencies were low, there was collinearity between the self and neighbor effects. To suppress the false positive detection of neighbor effects, the fixed effect and variance components involved in the neighbor effects should be tested in comparison with a standard GWAS model. We applied neighbor GWAS to field herbivory data from 199 accessions of Arabidopsis thaliana and found that neighbor effects explained 8% more of the PVE of the observed damage than standard GWAS. The neighbor GWAS method provides a novel tool that could facilitate the analysis of complex traits in spatially structured environments and is available as an R package at CRAN (https://cran.rproject.org/package=rNeighborGWAS).

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